The Enshittifinancial Crisis is not merely a critique of AI, but a diagnosis of a financial system that has lost its capacity for self-correction.

The Enshittifinancial Crisis: When Enshittification Reaches Capital Markets

by ChatGPT-5.2

Introduction

The Enshittifinancial Crisis extends Cory Doctorow’s concept of “enshittification” beyond digital platforms into the financial system itself. Zitron argues that what began as a degradation of user experience in tech products has metastasized into a systemic failure of capital allocation, corporate governance, and market accountability—accelerated and amplified by the current AI boom. The essay is less a narrow critique of artificial intelligence than a sweeping indictment of how modern finance, venture capital, and public markets have abandoned fundamentals in favor of narrative-driven speculation.

Main Grievances

1. The Enshittification of Capital Markets

Zitron’s central grievance is that stock markets no longer price companies based on business health, profitability, or long-term viability, but on the expectation that “number go up.” Analysts, investors, and the media have become complicit in a feedback loop where skepticism is punished and hype is rewarded. This has led to what he calls a fourth stage of enshittification: companies no longer just exploit users and business customers, but also mislead and abuse shareholders themselves.

2. AI as a Narrative Substitute for Revenue

Another core grievance is that “AI” has become a narrative placeholder for growth rather than a demonstrable source of sustainable revenue. Massive capital expenditures—particularly on data centers, GPUs, and long-lived infrastructure—are justified with vague promises of future AI monetization, even when companies provide little or no transparency about actual returns, utilization, or margins.

3. Analyst and Media Failure

Zitron sharply criticizes sell-side analysts and business media for amplifying announcements—letters of intent, non-binding partnerships, speculative projections—as if they were completed, revenue-backed deals. This behavior, he argues, borders on systemic misinformation and directly contributes to retail and institutional investors being misled.

4. Venture Capital’s Existential Trap

The essay argues that generative AI is structurally incompatible with the venture capital model. AI startups are capital-intensive, have worsening margins as they scale, and lack credible paths to IPOs or acquisitions. As a result, VCs are trapped holding illiquid assets while continuing to pour money into the same companies to preserve paper valuations and fee structures.

5. The Data Center and Debt Time Bomb

Finally, Zitron highlights the scale of leveraged debt underpinning the AI infrastructure boom—data center credit facilities, off-balance-sheet accounting, extended depreciation schedules—and warns that a demand shortfall or funding shock could trigger cascading failures across banks, hyperscalers, and the broader economy.

Most Surprising, Controversial, and Valuable Claims

Most Surprising

• The assertion that hundreds of billions in AI-related capex cannot be reconciled with reported GPU sales and disclosed infrastructure deployments, raising the question of where the money is actually going.

• The degree to which major “AI deals” turned out to be non-binding letters of intent with no revenue impact, even months later.

Most Controversial

• The claim that generative AI is inherently unprofitable at scale, not merely immature.

• The argument that analysts and investors are no longer misled, but actively choosing not to look too closely because skepticism threatens the entire system.

Most Valuable

• The framing of AI not as a technological revolution but as a financial coping mechanism for a post-hypergrowth tech sector.

• The concept of “enshittification stage four,” where capital markets themselves become degraded systems that punish truth-seeking behavior.

Points of Agreement and Disagreement

Where the Critique Is Convincing

The analysis of incentive misalignment is particularly strong. Zitron convincingly shows how analyst compensation, venture fee structures, and executive stock incentives converge toward short-term valuation inflation rather than long-term value creation. His skepticism toward AI deal announcements and accounting practices is well-founded and supported by concrete examples.

Where the Argument Overreaches

At times, the essay treats all AI development as economically doomed. While many current models and startups are clearly unsustainable, this risks underestimating the possibility of second-order efficiencies, domain-specific AI applications, or non-VC funding models that could yield durable value. The tone—intentionally polemical—also blurs the line between structural critique and rhetorical absolutism.

Recommendations for Stakeholders

For Investors and Analysts

• Treat “letters of intent” and “strategic partnerships” as non-events unless tied to binding contracts and disclosed revenue.

• Demand granular disclosure on AI capex: GPU counts, utilization rates, depreciation assumptions, and unit economics.

• Re-center valuation models on profitability and cash flow rather than narrative growth.

For Regulators and Policymakers

• Scrutinize off-balance-sheet financing, extended depreciation schedules, and data center credit structures.

• Strengthen disclosure requirements around material AI-related investments and counterparty risk.

• Reassess whether current securities regulations adequately address narrative-driven market manipulation.

For Venture Capital and Limited Partners

• Re-evaluate whether large-scale generative AI belongs in the venture asset class at all.

• Shift funding toward capital-efficient, domain-specific applications with clear paths to profitability.

• Increase transparency with LPs about liquidity risks, secondary-market reliance, and realistic exit scenarios.

For Technology Companies

• Stop using “AI” as a catch-all justification for unlimited spending.

• Align AI investment with demonstrable customer value and sustainable pricing models.

• Restore credibility through radical transparency rather than hype.

Conclusion

The Enshittifinancial Crisis is not merely a critique of AI, but a diagnosis of a financial system that has lost its capacity for self-correction. Its most important contribution is the warning that enshittification is no longer confined to apps and platforms—it now defines how capital itself is allocated. Whether or not one agrees with every prediction, the underlying message is clear: without renewed skepticism, transparency, and accountability, the AI boom risks becoming not just another bubble, but a systemic financial failure with consequences far beyond Silicon Valley.

Asking AI about the statement: It’s not whether the business model itself is profitable, it’s whether you can get investment for it

PASCAL HETZSCHOLDT

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21 SEPTEMBER 2023

Question 1 of 2 for AI services: With everything you know or you can find about Silicon Valley's business models, would you agree with the statement: It’s not whether the business model itself is profitable, it’s whether you can get investment for it

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